The major focus of this research group involves the computational analysis of disease-causing mutations from a structural standpoint. The primary technique employed is called homology model building, or "threading." This technique, coupled with the examination of surface charges and geometry, allows for an assessment of the discrete structural effect of a mutation on a protein, one which can help to discern the underlying cause of phenotypes characterizing a given genetic disorder. We have utilized this approach with numerous mutations observed in members of the homeodomain family. These proteins play a fundamental role in a diverse set of functions that include the specification of body plan, pattern formation, and cell fate determination during metazoan development. First, we examined mutations in the homeobox gene PITX2; several mutations within the PITX2 homeodomain region are responsible for the development of the related ocular disorders Rieger syndrome and iridogoniodysgenesis. Here, the threading-based analysis revealed that point mutations responsible for the development of these genetic disorders lead to the inability of PITX2 to adopt its proper structure and bind to the regulatory sequences of its target gene(s), which, in turn, affects its metabolic role in the cell. More recently, we have devoted our attention to understanding mutations in the DNA-binding region of a number of forkhead transcription factors that have been implicated in the development of diverse inherited disorders. One such study involved the examination of mutations in the winged-helix FOXC1 transcription factor, mutations that underlie Axenfeld-Rieger anterior eye segment defects. Computational analysis identified a point mutation (I87M) that putatively reduced the thermodynamic stability of the FOXC1 protein; parallel biochemical analyses on this mutant also indicated that the I87M mutation reduced FOXC1 protein stability. We have also studied point mutations in FOXP2 that are responsible for a severe speech and language disorder, as well as mutations in FOXP3 that lead to X-linked polyendocrinopathy, immune dysfunction and diarrhea (IPEX). In both cases, these point mutations led to dramatic changes in the charge distribution on the surface of these proteins, particularly in areas known to be responsible for DNA binding. These marked changes in both charge distribution and surface geometry may impair critical biological processes that involve protein surface recognition. Finally, molecular modeling studies on the viral oncoprotein Qin suggest that missense mutations observed in these proteins alter the DNA-binding surface of the Qin forkhead domain, possibly interfering with oncogenic transformation. Additional studies on the homeodomain proteins have centered on the evolutionary relationships between members of this protein family. All members of this family are characterized by a helix-turn-helix DNA-binding motif, and these proteins regulate various cellular processes by specifically binding to the transcriptional control region of a target gene. An evolutionary classification of 129 human homeodomain proteins, many of which are involved in inherited human disorders when mutated, indicates that these proteins segregate into six distinct classes; this classification is consistent with the known structural and functional characteristics of these proteins. This analysis, coupled with recent observations from the initial analysis of the human genome sequence, provides some insight as to the pattern of distribution of the homeobox genes within the genome and to the array of functions that can be performed by these proteins. As an outgrowth of our studies on the homeodomain class of proteins, we have developed and continue to maintain the Homeodomain Resource. This publicly-available database provides a curated collection of information that includes full-length homeodomain-containinng sequence data, experimentally-derived structures, protein-protein interaction data, DNA-binding sites, and mutations leading to human genetic disorders. In addition to basic research questions directly involving human disease genes, our group is involved in the development and application of automated methods for the analysis of sequence and expression data. We have developed a program called GeneLink, which enables researchers to analyze large data sets from studies of complex trait genetic disorders, such as cancer, diabetes, and hypertension. Unlike cystic fibrosis and Huntington?s disease, which are caused by single genes, complex trait disorders involve many genes along with environmental factors. Gene-mapping studies involving families with an unusually high incidence of such diseases become complicated, requiring the identification and comparison of hundreds (and perhaps thousands) of DNA markers in thousands of individuals. GeneLink enables researchers to store this information and mine it for relationships that might not be immediately apparent.